A method for analyzing power quality events in an electrical system includes processing electrical measurement data from or derived from energy-related signals captured by at least one metering device in the electrical system to generate at least one dynamic tolerance curve. Each dynamic tolerance curve of the at least one dynamic tolerance curve characterizes a response characteristic of the electrical system at a respective metering point in the electrical system. The method also includes analyzing the at least one dynamic tolerance curve to identify special cases which require further evaluation(s)/clarification to be discernable and/or actionable. The at least one dynamic tolerance curve may be regenerated or updated, and/or new or additional dynamic tolerance curves may be generated, to provide the further clarification. One or more actions affecting at least one component in the electrical system may be performed in response to an analysis of the curve(s).
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2. The method of claim 1, wherein providing the further clarification includes providing additional information on the regenerated or updated at least one dynamic tolerance curve, and/or on the new or additionally generated dynamic tolerance curves, to make the regenerated and/or updated at least one dynamic tolerance curve, and/or the the new or additionally generated dynamic tolerance curves, discernable and/or actionable by the system.
3. The method of claim 1, wherein providing the further clarification includes using at least one of statistical, machine learning, rules based and other computational method or methods, process or processes, or tool or tools to provide the further clarification.
5. The method of claim 1, wherein the further clarification is provided, at least in part, in response to one or more user inputs or interactions.
This invention relates to a system for providing further clarification in response to user inputs or interactions, particularly in the context of automated or semi-automated processes such as customer service, technical support, or information retrieval. The problem addressed is the need for dynamic, user-driven clarification to improve the accuracy and relevance of responses in interactive systems. The system includes a primary method for generating an initial response or output based on a given input, such as a user query or request. This initial response may be generated using natural language processing, machine learning models, or rule-based systems. The invention further includes a mechanism to detect when additional clarification is needed, either automatically or based on user feedback. When further clarification is required, the system provides supplementary information, explanations, or follow-up questions in response to one or more user inputs or interactions. These interactions may include explicit requests for clarification, selections from predefined options, or implicit signals such as repeated queries or user behavior patterns. The system may also adapt its clarification strategy based on the nature of the user input, such as adjusting the level of detail, format, or medium (e.g., text, visual aids, or voice) to better suit the user's needs. The goal is to enhance user satisfaction and efficiency by ensuring that responses are tailored to the specific context and requirements of the interaction. This approach is particularly useful in applications where ambiguity or complexity in user queries necessitates dynamic, interactive refinement of responses.
6. The method of claim 1, wherein providing the indication of the at least one power quality event includes quantifying the impact of the at least one power quality event one or more loads in the electrical system.
8. The method of claim 6, wherein the impact is quantified based on relative (%) of load impact, absolute load impact, etc.
9. The method of claim 7, wherein the best method is determined based on at least one of: the application, location within the system, type of data being analyzed, type of event being analyzed, impact of event, scope of event, changes over time periods, determined patterns, etc.
12. The method of claim 10, wherein the load loss characteristics include load sensitivity to electrical perturbations.
A system and method for monitoring and analyzing electrical load loss characteristics in power distribution networks. The invention addresses the challenge of detecting and mitigating inefficiencies in electrical power systems, particularly those caused by electrical perturbations such as voltage fluctuations, frequency variations, or transient disturbances. These perturbations can lead to energy losses, reduced equipment lifespan, and system instability. The method involves continuously measuring electrical parameters such as voltage, current, and frequency at various points in the power distribution network. These measurements are analyzed to determine the sensitivity of the load to electrical perturbations, which is quantified as load loss characteristics. By assessing how different loads respond to perturbations, the system identifies areas where energy losses are most significant. The method further includes adjusting power delivery parameters, such as voltage regulation or reactive power compensation, to minimize these losses and improve overall system efficiency. The system may also incorporate predictive algorithms to anticipate potential perturbations and proactively adjust power delivery to prevent energy losses. Additionally, the method can generate reports and alerts for operators, highlighting areas with high load sensitivity and recommending corrective actions. This approach ensures optimal power distribution, reduces energy waste, and enhances the reliability of the electrical grid.
13. The method of claim 11, wherein optimizing the boundaries or threshold lines includes identifying or removing statistical outliers.
14. The method of claim 11, wherein the boundaries or threshold lines are automatic statistical or machine learning deduced boundaries or threshold lines.
15. The method of claim 11, wherein the boundaries or threshold lines are used to indicate one or more potential load loss(es) in the electrical system.
16. The method of claim 12, wherein the load sensitivity is based on at least one of load/component/system/process type(s), load/component/system/process location(s), event magnitude(s), and event duration(s) in the electrical system.
This invention relates to methods for determining load sensitivity in electrical systems, addressing the challenge of optimizing system performance and reliability by dynamically assessing how different loads, components, or processes respond to electrical events. The method evaluates load sensitivity based on multiple factors, including the type of load, component, system, or process involved, their specific locations within the system, the magnitude of electrical events (such as voltage fluctuations or faults), and the duration of these events. By analyzing these parameters, the system can prioritize critical loads, allocate resources efficiently, and mitigate risks associated with electrical disturbances. The approach ensures that sensitive components or processes are protected while maintaining overall system stability. This adaptive assessment allows for real-time adjustments, improving energy distribution and reducing downtime in industrial, commercial, or residential electrical networks. The method integrates data from various system elements to provide a comprehensive understanding of vulnerability, enabling proactive management of electrical events.
17. The method of claim 14, wherein the boundaries or threshold lines automatically calculated by machine learning or statistical methods or tools correspond to optimal vertical moving boundaries or lines which have only one X value for any given Y value and the X-Y pairs create continuous boundaries or lines.
19. The method of claim 18, wherein the action affecting at least one component of the electrical system is automatically performed by a control system associated with the electrical system.
20. The method of claim 18, wherein the at least one component includes one or more loads monitored by the at least one metering device.
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July 3, 2019
November 8, 2022
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